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Segmentation-based quality control of structural MRI using the CAT12 toolbox.

Robert Dahnke1,2,3, Polona Kalc1,2, Gabriel Ziegler4

  • 1Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07747, Germany.

Gigascience
|November 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new quality assessment tool for structural MRI scans. It helps researchers and clinicians quickly identify and address image artifacts, improving data reliability in magnetic resonance imaging analysis.

Keywords:
MRIbrainmotion artifactsquality assessmentquality controlsegmentation

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis

Background:

  • Magnetic resonance image (MRI) quality significantly impacts analysis outcomes.
  • Variations in scanners, protocols, and artifacts like motion can bias results.
  • Reliable image quality assessment is crucial for identifying outliers.

Purpose of the Study:

  • To develop and validate a quality assessment framework for structural (T1-weighted) MRI.
  • To standardize image quality measures and create an integrated rating system.
  • To facilitate the identification of outliers, particularly those with motion artifacts.

Main Methods:

  • Utilized tissue classification within the SPM/CAT12 software.
  • Introduced and standardized multiple image quality measures into quality scales.
  • Combined measures into an integrated structural image quality rating.
  • Evaluated robustness using synthetic and real datasets.

Main Results:

  • The proposed quality measures are robust against simulated segmentation issues and variations in atrophy, age, sex, brain size, and disease.
  • The framework effectively facilitates the separation of motion artifacts by detecting within-protocol deviations.
  • The integrated rating system aids in the interpretation and rapid identification of outliers.

Conclusions:

  • The developed quality control framework is a simple yet powerful tool.
  • It is suitable for application in both research and clinical settings.
  • Enhances the reliability and validity of MRI data analysis.